Overview

Dataset statistics

Number of variables30
Number of observations15453
Missing cells33738
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 MiB
Average record size in memory801.4 B

Variable types

Categorical9
Numeric8
Boolean2
DateTime9
Unsupported2

Warnings

RADIUS_FROM_POLL_USED_FOR_CALCULATION has constant value "60" Constant
VISIT_DURING_OPEN_HOURS has constant value "True" Constant
DEVICE_ID_HASH has a high cardinality: 13644 distinct values High cardinality
SPATIALLY_DISTINCT_GEOHASH_KEY has a high cardinality: 366 distinct values High cardinality
CND_POLL_UUID has a high cardinality: 366 distinct values High cardinality
PRECINCT_ID has a high cardinality: 348 distinct values High cardinality
PRECINCT_NAME has a high cardinality: 351 distinct values High cardinality
POLLING_LOCATION_ADDRESS has a high cardinality: 365 distinct values High cardinality
POLLING_LOCATION_NAME has a high cardinality: 372 distinct values High cardinality
LOWER_MINUTES_WAITING is highly correlated with UPPER_MINUTES_WAITING and 2 other fieldsHigh correlation
UPPER_MINUTES_WAITING is highly correlated with LOWER_MINUTES_WAITING and 2 other fieldsHigh correlation
EXPECTED_MINUTES_WAITING is highly correlated with LOWER_MINUTES_WAITING and 2 other fieldsHigh correlation
HOME_GEOID is highly correlated with HOME_COUNTY_FIPSHigh correlation
HOME_COUNTY_FIPS is highly correlated with HOME_GEOIDHigh correlation
WAIT_TIME_MINUTES is highly correlated with LOWER_MINUTES_WAITING and 2 other fieldsHigh correlation
UPPER_MINUTES_WAITING is highly correlated with EXPECTED_MINUTES_WAITING and 1 other fieldsHigh correlation
EXPECTED_MINUTES_WAITING is highly correlated with UPPER_MINUTES_WAITING and 1 other fieldsHigh correlation
HOME_GEOID is highly correlated with HOME_COUNTY_FIPS and 2 other fieldsHigh correlation
HOME_COUNTY_FIPS is highly correlated with HOME_GEOID and 2 other fieldsHigh correlation
WAIT_TIME_MINUTES is highly correlated with UPPER_MINUTES_WAITING and 1 other fieldsHigh correlation
POLLING_LOCATION_CENSUS_TRACT is highly correlated with HOME_GEOID and 2 other fieldsHigh correlation
POLLING_LOCATION_COUNTY_FIPS is highly correlated with HOME_GEOID and 2 other fieldsHigh correlation
LOWER_MINUTES_WAITING is highly correlated with RADIUS_FROM_POLL_USED_FOR_CALCULATION and 1 other fieldsHigh correlation
UPPER_MINUTES_WAITING is highly correlated with RADIUS_FROM_POLL_USED_FOR_CALCULATION and 3 other fieldsHigh correlation
RADIUS_FROM_POLL_USED_FOR_CALCULATION is highly correlated with LOWER_MINUTES_WAITING and 3 other fieldsHigh correlation
EXPECTED_MINUTES_WAITING is highly correlated with UPPER_MINUTES_WAITING and 2 other fieldsHigh correlation
HOME_GEOID is highly correlated with HOME_COUNTY_FIPS and 4 other fieldsHigh correlation
HOME_COUNTY_FIPS is highly correlated with HOME_GEOID and 4 other fieldsHigh correlation
HAS_PING_IN_BUILDING is highly correlated with RADIUS_FROM_POLL_USED_FOR_CALCULATION and 3 other fieldsHigh correlation
WAIT_TIME_MINUTES is highly correlated with UPPER_MINUTES_WAITING and 2 other fieldsHigh correlation
VISIT_DURING_OPEN_HOURS is highly correlated with LOWER_MINUTES_WAITING and 7 other fieldsHigh correlation
POLLING_LOCATION_CENSUS_TRACT is highly correlated with HOME_GEOID and 2 other fieldsHigh correlation
POLLING_LOCATION_COUNTY_FIPS is highly correlated with HOME_GEOID and 2 other fieldsHigh correlation
EXPECTED_MINUTES_WAITING is highly correlated with LOWER_MINUTES_WAITING and 2 other fieldsHigh correlation
HOME_GEOID is highly correlated with HOME_COUNTY_FIPSHigh correlation
LOWER_MINUTES_WAITING is highly correlated with EXPECTED_MINUTES_WAITING and 2 other fieldsHigh correlation
WAIT_TIME_MINUTES is highly correlated with EXPECTED_MINUTES_WAITING and 2 other fieldsHigh correlation
UPPER_MINUTES_WAITING is highly correlated with EXPECTED_MINUTES_WAITING and 2 other fieldsHigh correlation
HOME_COUNTY_FIPS is highly correlated with HOME_GEOIDHigh correlation
VISIT_DURING_OPEN_HOURS is highly correlated with RADIUS_FROM_POLL_USED_FOR_CALCULATION and 2 other fieldsHigh correlation
RADIUS_FROM_POLL_USED_FOR_CALCULATION is highly correlated with VISIT_DURING_OPEN_HOURS and 2 other fieldsHigh correlation
HAS_PING_IN_BUILDING is highly correlated with VISIT_DURING_OPEN_HOURS and 1 other fieldsHigh correlation
POLLING_LOCATION_SOURCE is highly correlated with VISIT_DURING_OPEN_HOURS and 1 other fieldsHigh correlation
PRECINCT_NAME has 601 (3.9%) missing values Missing
HOME_GEOID has 1089 (7.0%) missing values Missing
HOME_COUNTY_FIPS has 1089 (7.0%) missing values Missing
TIMESTAMP_OPEN_LOCAL has 15453 (100.0%) missing values Missing
TIMESTAMP_CLOSE_LOCAL has 15453 (100.0%) missing values Missing
DEVICE_ID_HASH is uniformly distributed Uniform
TIMESTAMP_OPEN_LOCAL is an unsupported type, check if it needs cleaning or further analysis Unsupported
TIMESTAMP_CLOSE_LOCAL is an unsupported type, check if it needs cleaning or further analysis Unsupported
LOWER_MINUTES_WAITING has 4182 (27.1%) zeros Zeros

Reproduction

Analysis started2021-08-25 16:22:53.847155
Analysis finished2021-08-25 16:23:26.672614
Duration32.83 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

DEVICE_ID_HASH
Categorical

HIGH CARDINALITY
UNIFORM

Distinct13644
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
ac40ab73c49925fe10b700e1f89d7af7
 
25
d777e237f071227d4d4971b2e09fc848
 
15
85089c4495e45ae990a9e14782823d13
 
12
851ceebc7ed109a09a3027a930f22aa5
 
12
427b32b3b726dd7be14da5cb4489ec6c
 
12
Other values (13639)
15377 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters494496
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12259 ?
Unique (%)79.3%

Sample

1st rowcce5e2018329913be3ebcb4235f39760
2nd row6ac91bbf9674101f677eb93eab3d5b8e
3rd row9d7b72dd88ad3257ef3c5ba364db11ce
4th row7f574e294815ffc566b8f9b250d72717
5th rowdfb9aa0942eefa2b0c57eb6b2af415db

Common Values

ValueCountFrequency (%)
ac40ab73c49925fe10b700e1f89d7af725
 
0.2%
d777e237f071227d4d4971b2e09fc84815
 
0.1%
85089c4495e45ae990a9e14782823d1312
 
0.1%
851ceebc7ed109a09a3027a930f22aa512
 
0.1%
427b32b3b726dd7be14da5cb4489ec6c12
 
0.1%
443f198e0df4368d2e77da3c6842e1d08
 
0.1%
ecd79981c30a715a6f94f6795b09f9bb8
 
0.1%
e00917a18d4cc79633d510cbc7ffc1198
 
0.1%
9957134cfecff4ecf91d91bff7de1f558
 
0.1%
672982e6fd2a06b863466c772f0f24688
 
0.1%
Other values (13634)15337
99.2%

Length

2021-08-25T11:23:27.121059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac40ab73c49925fe10b700e1f89d7af725
 
0.2%
d777e237f071227d4d4971b2e09fc84815
 
0.1%
85089c4495e45ae990a9e14782823d1312
 
0.1%
851ceebc7ed109a09a3027a930f22aa512
 
0.1%
427b32b3b726dd7be14da5cb4489ec6c12
 
0.1%
443f198e0df4368d2e77da3c6842e1d08
 
0.1%
ecd79981c30a715a6f94f6795b09f9bb8
 
0.1%
e00917a18d4cc79633d510cbc7ffc1198
 
0.1%
9957134cfecff4ecf91d91bff7de1f558
 
0.1%
672982e6fd2a06b863466c772f0f24688
 
0.1%
Other values (13634)15337
99.2%

Most occurring characters

ValueCountFrequency (%)
931157
 
6.3%
431145
 
6.3%
c31109
 
6.3%
731108
 
6.3%
131078
 
6.3%
e31032
 
6.3%
a30975
 
6.3%
630925
 
6.3%
f30887
 
6.2%
b30834
 
6.2%
Other values (6)184246
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number308972
62.5%
Lowercase Letter185524
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
931157
10.1%
431145
10.1%
731108
10.1%
131078
10.1%
630925
10.0%
330809
10.0%
230804
10.0%
030743
10.0%
830719
9.9%
530484
9.9%
Lowercase Letter
ValueCountFrequency (%)
c31109
16.8%
e31032
16.7%
a30975
16.7%
f30887
16.6%
b30834
16.6%
d30687
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common308972
62.5%
Latin185524
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
931157
10.1%
431145
10.1%
731108
10.1%
131078
10.1%
630925
10.0%
330809
10.0%
230804
10.0%
030743
10.0%
830719
9.9%
530484
9.9%
Latin
ValueCountFrequency (%)
c31109
16.8%
e31032
16.7%
a30975
16.7%
f30887
16.6%
b30834
16.6%
d30687
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII494496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
931157
 
6.3%
431145
 
6.3%
c31109
 
6.3%
731108
 
6.3%
131078
 
6.3%
e31032
 
6.3%
a30975
 
6.3%
630925
 
6.3%
f30887
 
6.2%
b30834
 
6.2%
Other values (6)184246
37.3%

SPATIALLY_DISTINCT_GEOHASH_KEY
Categorical

HIGH CARDINALITY

Distinct366
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
9ze3x7s_general
 
164
9zmm1mm_general
 
163
9z7fdpv_general
 
157
9zmsc1s_general
 
131
9zmkgp6_general
 
130
Other values (361)
14708 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters231795
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9zqumhf_general
2nd row9zqumhf_general
3rd row9zw39fk_general
4th row9zw39fk_general
5th row9zw39fk_general

Common Values

ValueCountFrequency (%)
9ze3x7s_general164
 
1.1%
9zmm1mm_general163
 
1.1%
9z7fdpv_general157
 
1.0%
9zmsc1s_general131
 
0.8%
9zmkgp6_general130
 
0.8%
9zmmtfw_general128
 
0.8%
9zm7qcv_general124
 
0.8%
9zesgs9_general116
 
0.8%
9zmmekr_general108
 
0.7%
9zwhfzz_general104
 
0.7%
Other values (356)14128
91.4%

Length

2021-08-25T11:23:27.652526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9ze3x7s_general164
 
1.1%
9zmm1mm_general163
 
1.1%
9z7fdpv_general157
 
1.0%
9zmsc1s_general131
 
0.8%
9zmkgp6_general130
 
0.8%
9zmmtfw_general128
 
0.8%
9zm7qcv_general124
 
0.8%
9zesgs9_general116
 
0.8%
9zmmekr_general108
 
0.7%
9zwhfzz_general104
 
0.7%
Other values (356)14128
91.4%

Most occurring characters

ValueCountFrequency (%)
e33545
14.5%
r18609
 
8.0%
917898
 
7.7%
z17834
 
7.7%
g17249
 
7.4%
n17110
 
7.4%
_15453
 
6.7%
a15453
 
6.7%
l15453
 
6.7%
m9347
 
4.0%
Other values (25)53844
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter183823
79.3%
Decimal Number32519
 
14.0%
Connector Punctuation15453
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e33545
18.2%
r18609
10.1%
z17834
9.7%
g17249
9.4%
n17110
9.3%
a15453
8.4%
l15453
8.4%
m9347
 
5.1%
q4324
 
2.4%
k3960
 
2.2%
Other values (14)30939
16.8%
Decimal Number
ValueCountFrequency (%)
917898
55.0%
72948
 
9.1%
11975
 
6.1%
61943
 
6.0%
31881
 
5.8%
81343
 
4.1%
51274
 
3.9%
41271
 
3.9%
01018
 
3.1%
2968
 
3.0%
Connector Punctuation
ValueCountFrequency (%)
_15453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin183823
79.3%
Common47972
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e33545
18.2%
r18609
10.1%
z17834
9.7%
g17249
9.4%
n17110
9.3%
a15453
8.4%
l15453
8.4%
m9347
 
5.1%
q4324
 
2.4%
k3960
 
2.2%
Other values (14)30939
16.8%
Common
ValueCountFrequency (%)
917898
37.3%
_15453
32.2%
72948
 
6.1%
11975
 
4.1%
61943
 
4.1%
31881
 
3.9%
81343
 
2.8%
51274
 
2.7%
41271
 
2.6%
01018
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII231795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e33545
14.5%
r18609
 
8.0%
917898
 
7.7%
z17834
 
7.7%
g17249
 
7.4%
n17110
 
7.4%
_15453
 
6.7%
a15453
 
6.7%
l15453
 
6.7%
m9347
 
4.0%
Other values (25)53844
23.2%

CND_POLL_UUID
Categorical

HIGH CARDINALITY

Distinct366
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
167bae45d46c7ab8531cac047c2b45f3
 
164
14c1e42382aa0d67f75ea6ebb755fa68
 
163
7abe21bcb1b22033d82a131af606e5bd
 
157
b14899b33ff3ecfe4532d4f357e28750
 
131
7eeb48662ad3bcd0d7c342a9797f69b7
 
130
Other values (361)
14708 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters494496
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd77a78359a234e91d2167ef7847b6e79
2nd rowd77a78359a234e91d2167ef7847b6e79
3rd rowfce6d89b09db1889067453187930e4ee
4th rowfce6d89b09db1889067453187930e4ee
5th rowfce6d89b09db1889067453187930e4ee

Common Values

ValueCountFrequency (%)
167bae45d46c7ab8531cac047c2b45f3164
 
1.1%
14c1e42382aa0d67f75ea6ebb755fa68163
 
1.1%
7abe21bcb1b22033d82a131af606e5bd157
 
1.0%
b14899b33ff3ecfe4532d4f357e28750131
 
0.8%
7eeb48662ad3bcd0d7c342a9797f69b7130
 
0.8%
dedcfae7031ab4bd7b0948d57cf154dd128
 
0.8%
26b25b0c45d42526c531aae7ec914b59124
 
0.8%
1ed9f2858d08db04a8838c6a0b3510a5116
 
0.8%
0343b4b0c3d29edbf59abbf17b46c276108
 
0.7%
05f8ff96992fe212987f289ddb4f52c5104
 
0.7%
Other values (356)14128
91.4%

Length

2021-08-25T11:23:28.130406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
167bae45d46c7ab8531cac047c2b45f3164
 
1.1%
14c1e42382aa0d67f75ea6ebb755fa68163
 
1.1%
7abe21bcb1b22033d82a131af606e5bd157
 
1.0%
b14899b33ff3ecfe4532d4f357e28750131
 
0.8%
7eeb48662ad3bcd0d7c342a9797f69b7130
 
0.8%
dedcfae7031ab4bd7b0948d57cf154dd128
 
0.8%
26b25b0c45d42526c531aae7ec914b59124
 
0.8%
1ed9f2858d08db04a8838c6a0b3510a5116
 
0.8%
0343b4b0c3d29edbf59abbf17b46c276108
 
0.7%
05f8ff96992fe212987f289ddb4f52c5104
 
0.7%
Other values (356)14128
91.4%

Most occurring characters

ValueCountFrequency (%)
b33507
 
6.8%
533485
 
6.8%
332898
 
6.7%
432426
 
6.6%
032163
 
6.5%
930948
 
6.3%
630901
 
6.2%
c30683
 
6.2%
a30489
 
6.2%
230215
 
6.1%
Other values (6)176781
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number310266
62.7%
Lowercase Letter184230
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
533485
10.8%
332898
10.6%
432426
10.5%
032163
10.4%
930948
10.0%
630901
10.0%
230215
9.7%
129521
9.5%
828934
9.3%
728775
9.3%
Lowercase Letter
ValueCountFrequency (%)
b33507
18.2%
c30683
16.7%
a30489
16.5%
f30109
16.3%
e29882
16.2%
d29560
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common310266
62.7%
Latin184230
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
533485
10.8%
332898
10.6%
432426
10.5%
032163
10.4%
930948
10.0%
630901
10.0%
230215
9.7%
129521
9.5%
828934
9.3%
728775
9.3%
Latin
ValueCountFrequency (%)
b33507
18.2%
c30683
16.7%
a30489
16.5%
f30109
16.3%
e29882
16.2%
d29560
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII494496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b33507
 
6.8%
533485
 
6.8%
332898
 
6.7%
432426
 
6.6%
032163
 
6.5%
930948
 
6.3%
630901
 
6.2%
c30683
 
6.2%
a30489
 
6.2%
230215
 
6.1%
Other values (6)176781
35.7%

PRECINCT_ID
Categorical

HIGH CARDINALITY

Distinct348
Distinct (%)2.3%
Missing53
Missing (%)0.3%
Memory size960.4 KiB
22
 
164
GRIMES3
 
163
CB 05
 
157
02
 
136
CLAY 1 (109)
 
131
Other values (343)
14649 

Length

Max length53
Median length5
Mean length6.744220779
Min length1

Characters and Unicode

Total characters103861
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHS
2nd rowHS
3rd rowWATERLOO 1-6
4th rowWATERLOO 1-6
5th rowWATERLOO 1-6

Common Values

ValueCountFrequency (%)
22164
 
1.1%
GRIMES3163
 
1.1%
CB 05157
 
1.0%
02136
 
0.9%
CLAY 1 (109)131
 
0.8%
JOHN 4130
 
0.8%
ANK 12128
 
0.8%
INDIANOLA 4124
 
0.8%
08116
 
0.8%
SCC116
 
0.8%
Other values (338)14035
90.8%

Length

2021-08-25T11:23:28.667785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dm718
 
3.0%
1601
 
2.5%
cb504
 
2.1%
4444
 
1.9%
2404
 
1.7%
ank401
 
1.7%
cedar352
 
1.5%
3345
 
1.5%
05306
 
1.3%
city301
 
1.3%
Other values (363)19335
81.5%

Most occurring characters

ValueCountFrequency (%)
8311
 
8.0%
N6293
 
6.1%
15599
 
5.4%
A5522
 
5.3%
E5157
 
5.0%
R5065
 
4.9%
C4607
 
4.4%
O4530
 
4.4%
T4143
 
4.0%
24040
 
3.9%
Other values (31)50594
48.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter68996
66.4%
Decimal Number22128
 
21.3%
Space Separator8311
 
8.0%
Other Punctuation1295
 
1.2%
Open Punctuation1092
 
1.1%
Close Punctuation1092
 
1.1%
Dash Punctuation947
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N6293
 
9.1%
A5522
 
8.0%
E5157
 
7.5%
R5065
 
7.3%
C4607
 
6.7%
O4530
 
6.6%
T4143
 
6.0%
L3792
 
5.5%
I3748
 
5.4%
S3554
 
5.2%
Other values (15)22585
32.7%
Decimal Number
ValueCountFrequency (%)
15599
25.3%
24040
18.3%
03720
16.8%
32537
11.5%
41715
 
7.8%
51275
 
5.8%
71107
 
5.0%
9830
 
3.8%
6737
 
3.3%
8568
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/1082
83.6%
.213
 
16.4%
Space Separator
ValueCountFrequency (%)
8311
100.0%
Dash Punctuation
ValueCountFrequency (%)
-947
100.0%
Open Punctuation
ValueCountFrequency (%)
(1092
100.0%
Close Punctuation
ValueCountFrequency (%)
)1092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68996
66.4%
Common34865
33.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N6293
 
9.1%
A5522
 
8.0%
E5157
 
7.5%
R5065
 
7.3%
C4607
 
6.7%
O4530
 
6.6%
T4143
 
6.0%
L3792
 
5.5%
I3748
 
5.4%
S3554
 
5.2%
Other values (15)22585
32.7%
Common
ValueCountFrequency (%)
8311
23.8%
15599
16.1%
24040
11.6%
03720
10.7%
32537
 
7.3%
41715
 
4.9%
51275
 
3.7%
71107
 
3.2%
(1092
 
3.1%
)1092
 
3.1%
Other values (6)4377
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII103861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8311
 
8.0%
N6293
 
6.1%
15599
 
5.4%
A5522
 
5.3%
E5157
 
5.0%
R5065
 
4.9%
C4607
 
4.4%
O4530
 
4.4%
T4143
 
4.0%
24040
 
3.9%
Other values (31)50594
48.7%

PRECINCT_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct351
Distinct (%)2.4%
Missing601
Missing (%)3.9%
Memory size1.0 MiB
22 Morningside Branch Library
 
164
Grimes 1 (123)
 
163
Council Bluffs 01
 
157
Altoona 5 (91)
 
131
Johnston 4 (130)
 
130
Other values (346)
14107 

Length

Max length47
Median length14
Mean length14.9315917
Min length2

Characters and Unicode

Total characters221764
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHS
2nd rowHS
3rd rowCEDAR VALLEY CHURCH
4th rowCEDAR VALLEY CHURCH
5th rowCEDAR VALLEY CHURCH

Common Values

ValueCountFrequency (%)
22 Morningside Branch Library164
 
1.1%
Grimes 1 (123)163
 
1.1%
Council Bluffs 01157
 
1.0%
Altoona 5 (91)131
 
0.8%
Johnston 4 (130)130
 
0.8%
Ankeny 12 (103)128
 
0.8%
INDIANOLA 1124
 
0.8%
Sioux Center Central116
 
0.8%
Madison 1 (133)108
 
0.7%
Charles City 1 (Courthouse)104
 
0.7%
Other values (341)13527
87.5%
(Missing)601
 
3.9%

Length

2021-08-25T11:23:29.023559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11954
 
5.0%
moines1183
 
3.0%
des1183
 
3.0%
2933
 
2.4%
precinct788
 
2.0%
ward780
 
2.0%
city761
 
1.9%
cedar661
 
1.7%
rapids602
 
1.5%
ankeny558
 
1.4%
Other values (455)30049
76.2%

Most occurring characters

ValueCountFrequency (%)
24996
 
11.3%
e15615
 
7.0%
n13288
 
6.0%
o11141
 
5.0%
a11032
 
5.0%
r10765
 
4.9%
i9618
 
4.3%
s8314
 
3.7%
t8283
 
3.7%
l7698
 
3.5%
Other values (57)101014
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter127725
57.6%
Uppercase Letter36691
 
16.5%
Space Separator24996
 
11.3%
Decimal Number24348
 
11.0%
Open Punctuation2780
 
1.3%
Close Punctuation2780
 
1.3%
Other Punctuation1606
 
0.7%
Dash Punctuation838
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C5302
14.5%
M3119
 
8.5%
A2990
 
8.1%
W2520
 
6.9%
D2391
 
6.5%
L2257
 
6.2%
P2238
 
6.1%
B1952
 
5.3%
R1605
 
4.4%
T1466
 
4.0%
Other values (15)10851
29.6%
Lowercase Letter
ValueCountFrequency (%)
e15615
12.2%
n13288
10.4%
o11141
8.7%
a11032
8.6%
r10765
 
8.4%
i9618
 
7.5%
s8314
 
6.5%
t8283
 
6.5%
l7698
 
6.0%
d4960
 
3.9%
Other values (14)27011
21.1%
Decimal Number
ValueCountFrequency (%)
17640
31.4%
23598
14.8%
03440
14.1%
32876
 
11.8%
41791
 
7.4%
51380
 
5.7%
91101
 
4.5%
7925
 
3.8%
8843
 
3.5%
6754
 
3.1%
Other Punctuation
ValueCountFrequency (%)
/1214
75.6%
.214
 
13.3%
&91
 
5.7%
;87
 
5.4%
Space Separator
ValueCountFrequency (%)
24996
100.0%
Open Punctuation
ValueCountFrequency (%)
(2780
100.0%
Close Punctuation
ValueCountFrequency (%)
)2780
100.0%
Dash Punctuation
ValueCountFrequency (%)
-838
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin164416
74.1%
Common57348
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e15615
 
9.5%
n13288
 
8.1%
o11141
 
6.8%
a11032
 
6.7%
r10765
 
6.5%
i9618
 
5.8%
s8314
 
5.1%
t8283
 
5.0%
l7698
 
4.7%
C5302
 
3.2%
Other values (39)63360
38.5%
Common
ValueCountFrequency (%)
24996
43.6%
17640
 
13.3%
23598
 
6.3%
03440
 
6.0%
32876
 
5.0%
(2780
 
4.8%
)2780
 
4.8%
41791
 
3.1%
51380
 
2.4%
/1214
 
2.1%
Other values (8)4853
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII221764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24996
 
11.3%
e15615
 
7.0%
n13288
 
6.0%
o11141
 
5.0%
a11032
 
5.0%
r10765
 
4.9%
i9618
 
4.3%
s8314
 
3.7%
t8283
 
3.7%
l7698
 
3.5%
Other values (57)101014
45.6%

POLLING_LOCATION_ADDRESS
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4005 Morningside Ave, Sioux City, IA 51106, United States
 
164
801 W 1st St STE A, Grimes, IA 50111, United States
 
163
2447 Avenue B, Council Bluffs, IA 51501, United States
 
157
2890, 1 Ave South, Altoona, IA 50009, United States
 
131
5291 Stoney Creek Ct, Johnston, IA 50131, United States
 
130
Other values (360)
14708 

Length

Max length71
Median length49
Mean length47.90603766
Min length27

Characters and Unicode

Total characters740292
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110 Main St, Hills, IA 52235, United States
2nd row110 Main St, Hills, IA 52235, United States
3rd row3520 Ansborough Av Waterloo 50701
4th row3520 Ansborough Av Waterloo 50701
5th row3520 Ansborough Av Waterloo 50701

Common Values

ValueCountFrequency (%)
4005 Morningside Ave, Sioux City, IA 51106, United States164
 
1.1%
801 W 1st St STE A, Grimes, IA 50111, United States163
 
1.1%
2447 Avenue B, Council Bluffs, IA 51501, United States157
 
1.0%
2890, 1 Ave South, Altoona, IA 50009, United States131
 
0.8%
5291 Stoney Creek Ct, Johnston, IA 50131, United States130
 
0.8%
520 NW 36th St, Ankeny, IA 50023, United States128
 
0.8%
301 N Buxton St, Indianola, IA 50125, USA124
 
0.8%
230 St Andrews Way, Sioux Center, IA 51250, United States116
 
0.8%
309 Van Dorn St, Polk City, IA 50226, United States108
 
0.7%
30 Valley View Dr, Council Bluffs, IA 51503, United States104
 
0.7%
Other values (355)14128
91.4%

Length

2021-08-25T11:23:29.380569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ia14821
 
11.1%
united12487
 
9.3%
states12487
 
9.3%
st8313
 
6.2%
ave3574
 
2.7%
n1430
 
1.1%
w1419
 
1.1%
main1392
 
1.0%
rd1387
 
1.0%
s1361
 
1.0%
Other values (942)74983
56.1%

Most occurring characters

ValueCountFrequency (%)
118201
 
16.0%
t59393
 
8.0%
e48730
 
6.6%
,42989
 
5.8%
n30915
 
4.2%
030196
 
4.1%
a27962
 
3.8%
S27214
 
3.7%
i25764
 
3.5%
525168
 
3.4%
Other values (56)303760
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter314027
42.4%
Decimal Number137862
18.6%
Uppercase Letter126264
17.1%
Space Separator118201
 
16.0%
Other Punctuation43708
 
5.9%
Dash Punctuation178
 
< 0.1%
Open Punctuation26
 
< 0.1%
Close Punctuation26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t59393
18.9%
e48730
15.5%
n30915
9.8%
a27962
8.9%
i25764
8.2%
s23625
 
7.5%
d20178
 
6.4%
o14392
 
4.6%
r12568
 
4.0%
l11526
 
3.7%
Other values (15)38974
12.4%
Uppercase Letter
ValueCountFrequency (%)
S27214
21.6%
A22769
18.0%
I16066
12.7%
U14026
11.1%
C5331
 
4.2%
W5198
 
4.1%
M4991
 
4.0%
D4179
 
3.3%
E3861
 
3.1%
N3771
 
3.0%
Other values (14)18858
14.9%
Decimal Number
ValueCountFrequency (%)
030196
21.9%
525168
18.3%
122943
16.6%
219717
14.3%
312237
8.9%
68133
 
5.9%
47677
 
5.6%
74684
 
3.4%
93595
 
2.6%
83512
 
2.5%
Other Punctuation
ValueCountFrequency (%)
,42989
98.4%
.384
 
0.9%
#335
 
0.8%
Space Separator
ValueCountFrequency (%)
118201
100.0%
Open Punctuation
ValueCountFrequency (%)
(26
100.0%
Close Punctuation
ValueCountFrequency (%)
)26
100.0%
Dash Punctuation
ValueCountFrequency (%)
-178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin440291
59.5%
Common300001
40.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t59393
13.5%
e48730
 
11.1%
n30915
 
7.0%
a27962
 
6.4%
S27214
 
6.2%
i25764
 
5.9%
s23625
 
5.4%
A22769
 
5.2%
d20178
 
4.6%
I16066
 
3.6%
Other values (39)137675
31.3%
Common
ValueCountFrequency (%)
118201
39.4%
,42989
 
14.3%
030196
 
10.1%
525168
 
8.4%
122943
 
7.6%
219717
 
6.6%
312237
 
4.1%
68133
 
2.7%
47677
 
2.6%
74684
 
1.6%
Other values (7)8056
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII740292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118201
 
16.0%
t59393
 
8.0%
e48730
 
6.6%
,42989
 
5.8%
n30915
 
4.2%
030196
 
4.1%
a27962
 
3.8%
S27214
 
3.7%
i25764
 
3.5%
525168
 
3.4%
Other values (56)303760
41.0%

LOWER_MINUTES_WAITING
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2287
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.776529909
Minimum0
Maximum178.6
Zeros4182
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:29.618616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.666667
Q311.316667
95-th percentile31.866667
Maximum178.6
Range178.6
Interquartile range (IQR)11.316667

Descriptive statistics

Standard deviation14.77204733
Coefficient of variation (CV)1.683130745
Kurtosis28.30738284
Mean8.776529909
Median Absolute Deviation (MAD)3.666667
Skewness4.345674881
Sum135623.7167
Variance218.2133822
MonotonicityNot monotonic
2021-08-25T11:23:29.852851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04182
27.1%
1245
 
1.6%
0.166667183
 
1.2%
0.833333130
 
0.8%
968
 
0.4%
1062
 
0.4%
0.01666761
 
0.4%
1158
 
0.4%
848
 
0.3%
240
 
0.3%
Other values (2277)10376
67.1%
ValueCountFrequency (%)
04182
27.1%
0.01666761
 
0.4%
0.03333338
 
0.2%
0.0530
 
0.2%
0.06666722
 
0.1%
0.08333332
 
0.2%
0.122
 
0.1%
0.11666722
 
0.1%
0.13333319
 
0.1%
0.1522
 
0.1%
ValueCountFrequency (%)
178.61
< 0.1%
173.3833331
< 0.1%
172.251
< 0.1%
165.8666671
< 0.1%
164.951
< 0.1%
164.7833331
< 0.1%
161.6666671
< 0.1%
161.2333331
< 0.1%
154.7166671
< 0.1%
154.2333331
< 0.1%

UPPER_MINUTES_WAITING
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3474
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.7053021
Minimum0
Maximum181.033333
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:30.073812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.85
Q19.683333
median14.85
Q323.883333
95-th percentile66.0800002
Maximum181.033333
Range181.033333
Interquartile range (IQR)14.2

Descriptive statistics

Standard deviation23.31894143
Coefficient of variation (CV)1.074343095
Kurtosis12.83229447
Mean21.7053021
Median Absolute Deviation (MAD)6.466667
Skewness3.236965219
Sum335412.0333
Variance543.7730293
MonotonicityNot monotonic
2021-08-25T11:23:30.234210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381
 
0.5%
1269
 
0.4%
1066
 
0.4%
1163
 
0.4%
1362
 
0.4%
1660
 
0.4%
451
 
0.3%
1548
 
0.3%
547
 
0.3%
946
 
0.3%
Other values (3464)14860
96.2%
ValueCountFrequency (%)
02
< 0.1%
0.1666671
 
< 0.1%
21
 
< 0.1%
2.0166671
 
< 0.1%
2.0333331
 
< 0.1%
2.051
 
< 0.1%
2.0666673
< 0.1%
2.0833331
 
< 0.1%
2.12
< 0.1%
2.1166673
< 0.1%
ValueCountFrequency (%)
181.0333331
< 0.1%
180.5333331
< 0.1%
179.7666671
< 0.1%
179.3333331
< 0.1%
178.61
< 0.1%
177.7166671
< 0.1%
177.1833331
< 0.1%
177.1666671
< 0.1%
176.551
< 0.1%
176.1333331
< 0.1%

RADIUS_FROM_POLL_USED_FOR_CALCULATION
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.5 KiB
60
15453 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30906
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
6015453
100.0%

Length

2021-08-25T11:23:30.512438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-25T11:23:30.596067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
6015453
100.0%

Most occurring characters

ValueCountFrequency (%)
615453
50.0%
015453
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30906
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
615453
50.0%
015453
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common30906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
615453
50.0%
015453
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615453
50.0%
015453
50.0%

EXPECTED_MINUTES_WAITING
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3508
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.98698958
Minimum0
Maximum179.533333
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:30.700972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.233333
Q19.166667
median14.25
Q323.016667
95-th percentile65.18
Maximum179.533333
Range179.533333
Interquartile range (IQR)13.85

Descriptive statistics

Standard deviation23.06016048
Coefficient of variation (CV)1.098783625
Kurtosis12.9154431
Mean20.98698958
Median Absolute Deviation (MAD)6.333333
Skewness3.247615723
Sum324311.9499
Variance531.7710011
MonotonicityNot monotonic
2021-08-25T11:23:30.884817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025
 
0.2%
1224
 
0.2%
323
 
0.1%
11.78333322
 
0.1%
10.63333322
 
0.1%
9.83333322
 
0.1%
9.68333322
 
0.1%
921
 
0.1%
9.76666721
 
0.1%
9.91666721
 
0.1%
Other values (3498)15230
98.6%
ValueCountFrequency (%)
014
0.1%
0.1833331
 
< 0.1%
0.251
 
< 0.1%
0.4166671
 
< 0.1%
0.4666672
 
< 0.1%
0.5666671
 
< 0.1%
0.5833331
 
< 0.1%
0.61
 
< 0.1%
0.651
 
< 0.1%
0.7166672
 
< 0.1%
ValueCountFrequency (%)
179.5333331
< 0.1%
178.61
< 0.1%
177.0833331
< 0.1%
177.051
< 0.1%
176.31
< 0.1%
175.8666671
< 0.1%
175.5166671
< 0.1%
174.5166671
< 0.1%
173.651
< 0.1%
173.3833331
< 0.1%

HOME_GEOID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1925
Distinct (%)13.4%
Missing1089
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1.919411145 × 1011
Minimum1.70159603 × 1011
Maximum5.50659704 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:31.063780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.70159603 × 1011
5-th percentile1.90130028 × 1011
Q11.906101011 × 1011
median1.91250302 × 1011
Q31.91530113 × 1011
95-th percentile1.91839605 × 1011
Maximum5.50659704 × 1011
Range3.80500101 × 1011
Interquartile range (IQR)920011953

Descriptive statistics

Standard deviation1.244433103 × 1010
Coefficient of variation (CV)0.06483410841
Kurtosis422.3913422
Mean1.919411145 × 1011
Median Absolute Deviation (MAD)300011000
Skewness18.34890829
Sum2.757042169 × 1015
Variance1.548613747 × 1020
MonotonicityNot monotonic
2021-08-25T11:23:31.227652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.91530102 × 1011250
 
1.6%
1.915301071 × 1011117
 
0.8%
1.91530101 × 101190
 
0.6%
1.91530113 × 101175
 
0.5%
1.91130001 × 101172
 
0.5%
1.91530117 × 101171
 
0.5%
1.91810202 × 101161
 
0.4%
1.91530115 × 101161
 
0.4%
1.91530115 × 101160
 
0.4%
1.904905081 × 101160
 
0.4%
Other values (1915)13447
87.0%
(Missing)1089
 
7.0%
ValueCountFrequency (%)
1.70159603 × 10111
< 0.1%
1.7067954 × 10111
< 0.1%
1.70679541 × 10111
< 0.1%
1.70679541 × 10112
< 0.1%
1.70730301 × 10111
< 0.1%
1.70730302 × 10111
< 0.1%
1.70730302 × 10111
< 0.1%
1.70730312 × 10111
< 0.1%
1.70730312 × 10111
< 0.1%
1.70850202 × 10111
< 0.1%
ValueCountFrequency (%)
5.50659704 × 10111
 
< 0.1%
5.50439612 × 10113
< 0.1%
5.50439608 × 10111
 
< 0.1%
5.50439605 × 10111
 
< 0.1%
5.50239605 × 10111
 
< 0.1%
4.61270203 × 10112
< 0.1%
4.61270203 × 10111
 
< 0.1%
3.11770503 × 10111
 
< 0.1%
3.11770501 × 10111
 
< 0.1%
3.11559684 × 10112
< 0.1%

HOME_COUNTY_FIPS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct123
Distinct (%)0.9%
Missing1089
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean19193.91841
Minimum17015
Maximum55065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:31.384486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17015
5-th percentile19013
Q119061
median19125
Q319153
95-th percentile19183
Maximum55065
Range38050
Interquartile range (IQR)92

Descriptive statistics

Standard deviation1244.430023
Coefficient of variation (CV)0.06483460002
Kurtosis422.3688122
Mean19193.91841
Median Absolute Deviation (MAD)30
Skewness18.34840106
Sum275701444
Variance1548606.081
MonotonicityNot monotonic
2021-08-25T11:23:31.653368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191533148
20.4%
191131391
 
9.0%
19155778
 
5.0%
19049694
 
4.5%
19013433
 
2.8%
19061432
 
2.8%
19193424
 
2.7%
19181419
 
2.7%
19169385
 
2.5%
19099352
 
2.3%
Other values (113)5908
38.2%
(Missing)1089
 
7.0%
ValueCountFrequency (%)
170151
 
< 0.1%
170674
 
< 0.1%
170735
 
< 0.1%
170859
0.1%
171311
 
< 0.1%
1716119
0.1%
171872
 
< 0.1%
171954
 
< 0.1%
190014
 
< 0.1%
190031
 
< 0.1%
ValueCountFrequency (%)
550651
 
< 0.1%
550435
< 0.1%
550231
 
< 0.1%
461273
 
< 0.1%
311772
 
< 0.1%
311552
 
< 0.1%
3115312
0.1%
311311
 
< 0.1%
311271
 
< 0.1%
311091
 
< 0.1%

HAS_PING_IN_BUILDING
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
False
9130 
True
6323 
ValueCountFrequency (%)
False9130
59.1%
True6323
40.9%
2021-08-25T11:23:31.835401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

WAIT_TIME_MINUTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3508
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.98698958
Minimum0
Maximum179.533333
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:31.953459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.233333
Q19.166667
median14.25
Q323.016667
95-th percentile65.18
Maximum179.533333
Range179.533333
Interquartile range (IQR)13.85

Descriptive statistics

Standard deviation23.06016048
Coefficient of variation (CV)1.098783625
Kurtosis12.9154431
Mean20.98698958
Median Absolute Deviation (MAD)6.333333
Skewness3.247615723
Sum324311.9499
Variance531.7710011
MonotonicityNot monotonic
2021-08-25T11:23:32.121833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025
 
0.2%
1224
 
0.2%
323
 
0.1%
11.78333322
 
0.1%
10.63333322
 
0.1%
9.83333322
 
0.1%
9.68333322
 
0.1%
921
 
0.1%
9.76666721
 
0.1%
9.91666721
 
0.1%
Other values (3498)15230
98.6%
ValueCountFrequency (%)
014
0.1%
0.1833331
 
< 0.1%
0.251
 
< 0.1%
0.4166671
 
< 0.1%
0.4666672
 
< 0.1%
0.5666671
 
< 0.1%
0.5833331
 
< 0.1%
0.61
 
< 0.1%
0.651
 
< 0.1%
0.7166672
 
< 0.1%
ValueCountFrequency (%)
179.5333331
< 0.1%
178.61
< 0.1%
177.0833331
< 0.1%
177.051
< 0.1%
176.31
< 0.1%
175.8666671
< 0.1%
175.5166671
< 0.1%
174.5166671
< 0.1%
173.651
< 0.1%
173.3833331
< 0.1%
Distinct12200
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-03 00:00:02
Maximum2020-11-03 23:59:56
2021-08-25T11:23:32.280930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:32.431066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11176
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-02 23:00:41
Maximum2020-11-03 23:59:56
2021-08-25T11:23:32.629935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:32.794930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11976
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-02 23:00:41
Maximum2020-11-03 23:59:53
2021-08-25T11:23:33.039872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:33.245100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11433
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-03 00:00:39
Maximum2020-11-04 00:56:25
2021-08-25T11:23:33.651003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:33.801595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct12200
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-03 00:00:02
Maximum2020-11-03 23:59:56
2021-08-25T11:23:34.035245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:34.265069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11433
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-03 00:00:39
Maximum2020-11-04 00:56:25
2021-08-25T11:23:34.437675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:34.618212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11976
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-02 23:00:41
Maximum2020-11-03 23:59:53
2021-08-25T11:23:34.795613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:34.986864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11176
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2020-11-02 23:00:41
Maximum2020-11-03 23:59:56
2021-08-25T11:23:35.169488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:35.343729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

POLLING_LOCATION_SOURCE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1007.3 KiB
correction
14167 
google_api
 
632
cpi
 
601
DemocracyWorks
 
53

Length

Max length14
Median length10
Mean length9.741474147
Min length3

Characters and Unicode

Total characters150535
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcorrection
2nd rowcorrection
3rd rowgoogle_api
4th rowgoogle_api
5th rowgoogle_api

Common Values

ValueCountFrequency (%)
correction14167
91.7%
google_api632
 
4.1%
cpi601
 
3.9%
DemocracyWorks53
 
0.3%

Length

2021-08-25T11:23:35.619422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-25T11:23:35.706066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
correction14167
91.7%
google_api632
 
4.1%
cpi601
 
3.9%
democracyworks53
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o29704
19.7%
c29041
19.3%
r28440
18.9%
i15400
10.2%
e14852
9.9%
t14167
9.4%
n14167
9.4%
g1264
 
0.8%
p1233
 
0.8%
a685
 
0.5%
Other values (8)1582
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter149797
99.5%
Connector Punctuation632
 
0.4%
Uppercase Letter106
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o29704
19.8%
c29041
19.4%
r28440
19.0%
i15400
10.3%
e14852
9.9%
t14167
9.5%
n14167
9.5%
g1264
 
0.8%
p1233
 
0.8%
a685
 
0.5%
Other values (5)844
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
D53
50.0%
W53
50.0%
Connector Punctuation
ValueCountFrequency (%)
_632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin149903
99.6%
Common632
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o29704
19.8%
c29041
19.4%
r28440
19.0%
i15400
10.3%
e14852
9.9%
t14167
9.5%
n14167
9.5%
g1264
 
0.8%
p1233
 
0.8%
a685
 
0.5%
Other values (7)950
 
0.6%
Common
ValueCountFrequency (%)
_632
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII150535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o29704
19.7%
c29041
19.3%
r28440
18.9%
i15400
10.2%
e14852
9.9%
t14167
9.4%
n14167
9.4%
g1264
 
0.8%
p1233
 
0.8%
a685
 
0.5%
Other values (8)1582
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.9 KiB
Minimum2021-08-23 13:59:14.704000
Maximum2021-08-23 14:28:13.118000
2021-08-25T11:23:35.784526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:35.898233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

VISIT_DURING_OPEN_HOURS
Boolean

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
True
15453 
ValueCountFrequency (%)
True15453
100.0%
2021-08-25T11:23:36.001236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

TIMESTAMP_OPEN_LOCAL
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing15453
Missing (%)100.0%
Memory size120.9 KiB

TIMESTAMP_CLOSE_LOCAL
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing15453
Missing (%)100.0%
Memory size120.9 KiB

POLLING_LOCATION_CENSUS_TRACT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct315
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.911423058 × 1010
Minimum1.90059603 × 1010
Maximum1.91976805 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:36.103122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.90059603 × 1010
5-th percentile1.9013003 × 1010
Q11.90610102 × 1010
median1.91279502 × 1010
Q31.91530113 × 1010
95-th percentile1.91839603 × 1010
Maximum1.91976805 × 1010
Range191720200
Interquartile range (IQR)92001103

Descriptive statistics

Standard deviation53472367.33
Coefficient of variation (CV)0.002797516076
Kurtosis-0.9936070966
Mean1.911423058 × 1010
Median Absolute Deviation (MAD)28909400
Skewness-0.5251805639
Sum2.953722051 × 1014
Variance2.859294068 × 1015
MonotonicityNot monotonic
2021-08-25T11:23:36.276033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.915301022 × 1010212
 
1.4%
1.915301071 × 1010210
 
1.4%
1.91550317 × 1010205
 
1.3%
1.915301020 × 1010177
 
1.1%
1.9193002 × 1010164
 
1.1%
1.91530113 × 1010163
 
1.1%
1.91550306 × 1010157
 
1.0%
1.91530101 × 1010151
 
1.0%
1.91530115 × 1010144
 
0.9%
1.915301021 × 1010139
 
0.9%
Other values (305)13731
88.9%
ValueCountFrequency (%)
1.90059603 × 101035
0.2%
1.90079503 × 101034
 
0.2%
1.90090703 × 101026
 
0.2%
1.90119601 × 101034
 
0.2%
1.90119603 × 101087
0.6%
1.90119604 × 101043
0.3%
1.90119605 × 101047
0.3%
1.90119606 × 101026
 
0.2%
1.90130011 × 101025
 
0.2%
1.90130012 × 101042
0.3%
ValueCountFrequency (%)
1.91976805 × 101050
 
0.3%
1.91976803 × 101026
 
0.2%
1.91976801 × 101040
 
0.3%
1.91956901 × 101039
 
0.3%
1.91930033 × 101069
0.4%
1.91930032 × 101035
 
0.2%
1.91930021 × 101031
 
0.2%
1.91930021 × 101089
0.6%
1.9193002 × 1010164
1.1%
1.91930018 × 101035
 
0.2%

POLLING_LOCATION_COUNTY_FIPS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18998.40089
Minimum195
Maximum19197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.9 KiB
2021-08-25T11:23:36.436490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum195
5-th percentile19013
Q119061
median19127
Q319153
95-th percentile19183
Maximum19197
Range19002
Interquartile range (IQR)92

Descriptive statistics

Standard deviation1479.724807
Coefficient of variation (CV)0.07788680825
Kurtosis157.3100423
Mean18998.40089
Median Absolute Deviation (MAD)28
Skewness-12.61275216
Sum293582289
Variance2189585.506
MonotonicityNot monotonic
2021-08-25T11:23:36.597190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191533506
22.7%
191131508
 
9.8%
19155919
 
5.9%
19049674
 
4.4%
19193512
 
3.3%
19061475
 
3.1%
19013452
 
2.9%
19181434
 
2.8%
19163403
 
2.6%
19099392
 
2.5%
Other values (63)6178
40.0%
ValueCountFrequency (%)
19535
 
0.2%
19734
 
0.2%
19926
 
0.2%
19011237
1.5%
19013452
2.9%
19015212
1.4%
19019188
1.2%
1902157
 
0.4%
1902531
 
0.2%
19027121
 
0.8%
ValueCountFrequency (%)
19197116
 
0.8%
1919539
 
0.3%
19193512
3.3%
1918930
 
0.2%
1918388
 
0.6%
19181434
2.8%
1917932
 
0.2%
1917327
 
0.2%
19169370
2.4%
19167243
1.6%

POLLING_LOCATION_NAME
Categorical

HIGH CARDINALITY

Distinct372
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
22 MORNINGSIDE BRANCH LIBRARY
 
164
123 GRIMES UNITED METHODIST CHURCH
 
163
05 EPWORTH METHODIST CHURCH
 
157
091 ALTOONA CHRISTIAN CHURCH
 
131
130 STONEY CREEK HOTEL & CONFERENCE CENTER
 
130
Other values (367)
14708 

Length

Max length68
Median length26
Mean length26.68278004
Min length5

Characters and Unicode

Total characters412329
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHills Community Center
2nd rowHills Community Center
3rd rowCEDAR VALLEY CHURCH
4th rowCEDAR VALLEY CHURCH
5th rowCEDAR VALLEY CHURCH

Common Values

ValueCountFrequency (%)
22 MORNINGSIDE BRANCH LIBRARY164
 
1.1%
123 GRIMES UNITED METHODIST CHURCH163
 
1.1%
05 EPWORTH METHODIST CHURCH157
 
1.0%
091 ALTOONA CHRISTIAN CHURCH131
 
0.8%
130 STONEY CREEK HOTEL & CONFERENCE CENTER130
 
0.8%
103 LUTHERAN CHURCH OF HOPE ANKENY128
 
0.8%
SCC TERRACE VIEW EVENT CENTER116
 
0.8%
133 POLK CITY COMMUNITY BUILDING108
 
0.7%
10A NEW HORIZON PRESBYTERIAN CHURCH104
 
0.7%
CLAY REG EVENT CTR 2100
 
0.6%
Other values (362)14152
91.6%

Length

2021-08-25T11:23:36.970162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
church4357
 
6.8%
center3884
 
6.1%
community2212
 
3.5%
hall1598
 
2.5%
city1355
 
2.1%
methodist1166
 
1.8%
united895
 
1.4%
846
 
1.3%
library842
 
1.3%
lutheran800
 
1.3%
Other values (672)45746
71.8%

Most occurring characters

ValueCountFrequency (%)
48312
 
11.7%
E31531
 
7.6%
C27840
 
6.8%
R26504
 
6.4%
T25585
 
6.2%
N24392
 
5.9%
I22640
 
5.5%
A20512
 
5.0%
O20478
 
5.0%
H18567
 
4.5%
Other values (58)145968
35.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter323599
78.5%
Space Separator48312
 
11.7%
Decimal Number18930
 
4.6%
Lowercase Letter15184
 
3.7%
Other Punctuation3236
 
0.8%
Dash Punctuation1406
 
0.3%
Open Punctuation831
 
0.2%
Close Punctuation831
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E31531
 
9.7%
C27840
 
8.6%
R26504
 
8.2%
T25585
 
7.9%
N24392
 
7.5%
I22640
 
7.0%
A20512
 
6.3%
O20478
 
6.3%
H18567
 
5.7%
L18358
 
5.7%
Other values (16)87192
26.9%
Lowercase Letter
ValueCountFrequency (%)
o2010
13.2%
n1399
9.2%
t1288
 
8.5%
r1253
 
8.3%
e1211
 
8.0%
u1179
 
7.8%
i1047
 
6.9%
l888
 
5.8%
h800
 
5.3%
a750
 
4.9%
Other values (11)3359
22.1%
Decimal Number
ValueCountFrequency (%)
14160
22.0%
03699
19.5%
32627
13.9%
22593
13.7%
91499
 
7.9%
51249
 
6.6%
41008
 
5.3%
7801
 
4.2%
6716
 
3.8%
8578
 
3.1%
Other Punctuation
ValueCountFrequency (%)
:1248
38.6%
.776
24.0%
/516
15.9%
&321
 
9.9%
'253
 
7.8%
#93
 
2.9%
,29
 
0.9%
Space Separator
ValueCountFrequency (%)
48312
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1406
100.0%
Open Punctuation
ValueCountFrequency (%)
(831
100.0%
Close Punctuation
ValueCountFrequency (%)
)831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338783
82.2%
Common73546
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E31531
 
9.3%
C27840
 
8.2%
R26504
 
7.8%
T25585
 
7.6%
N24392
 
7.2%
I22640
 
6.7%
A20512
 
6.1%
O20478
 
6.0%
H18567
 
5.5%
L18358
 
5.4%
Other values (37)102376
30.2%
Common
ValueCountFrequency (%)
48312
65.7%
14160
 
5.7%
03699
 
5.0%
32627
 
3.6%
22593
 
3.5%
91499
 
2.0%
-1406
 
1.9%
51249
 
1.7%
:1248
 
1.7%
41008
 
1.4%
Other values (11)5745
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII412329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48312
 
11.7%
E31531
 
7.6%
C27840
 
6.8%
R26504
 
6.4%
T25585
 
6.2%
N24392
 
5.9%
I22640
 
5.5%
A20512
 
5.0%
O20478
 
5.0%
H18567
 
4.5%
Other values (58)145968
35.4%

Interactions

2021-08-25T11:23:13.365858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:13.565533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:13.757296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:13.891900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.009969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.128044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.276585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.440506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.573822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.701864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.828856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:14.943174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.058913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.174493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.355345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.567090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.750801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:15.897181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:16.042188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:16.199341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:16.368839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:16.557097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:16.865738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.020388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.175543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.332813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.530677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.752992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:17.933800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.107195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.258546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.411203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.564839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.717873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:18.906833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.072299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.223845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.373996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.524697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.709330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:19.871594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.019780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.169394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.332981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.497510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.683759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:20.839635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:21.015462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:21.165482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:21.446149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:21.679867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:21.887036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:22.038137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:22.194032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:22.369332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:22.607693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:22.843183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.018815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.157955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.293871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.445530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.599167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.766857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-25T11:23:23.925895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-08-25T11:23:37.139934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-25T11:23:37.381662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-25T11:23:37.607046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-25T11:23:37.844963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-25T11:23:38.035873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-25T11:23:24.301545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-25T11:23:25.601398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-25T11:23:26.127832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-25T11:23:26.303180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DEVICE_ID_HASHSPATIALLY_DISTINCT_GEOHASH_KEYCND_POLL_UUIDPRECINCT_IDPRECINCT_NAMEPOLLING_LOCATION_ADDRESSLOWER_MINUTES_WAITINGUPPER_MINUTES_WAITINGRADIUS_FROM_POLL_USED_FOR_CALCULATIONEXPECTED_MINUTES_WAITINGHOME_GEOIDHOME_COUNTY_FIPSHAS_PING_IN_BUILDINGWAIT_TIME_MINUTESLAST_PING_IN_CLUSTER_LOCAL_TIMEFIRST_PING_IN_CLUSTER_LOCAL_TIMELAST_PING_IN_PREVIOUS_CLUSTER_LOCAL_TIMEFIRST_PING_IN_NEXT_CLUSTER_LOCAL_TIMEEARLIEST_TIME_LEFT_POLLS_LOCAL_TIMELATEST_TIME_LEFT_POLLS_LOCAL_TIMEEARLIEST_TIME_ARRIVED_POLLS_LOCAL_TIMELATEST_TIME_ARRIVED_POLLS_LOCAL_TIMEPOLLING_LOCATION_SOURCECALCULATED_ATVISIT_DURING_OPEN_HOURSTIMESTAMP_OPEN_LOCALTIMESTAMP_CLOSE_LOCALPOLLING_LOCATION_CENSUS_TRACTPOLLING_LOCATION_COUNTY_FIPSPOLLING_LOCATION_NAME
0cce5e2018329913be3ebcb4235f397609zqumhf_generald77a78359a234e91d2167ef7847b6e79HSHS110 Main St, Hills, IA 52235, United States10.91666714.2333336013.8833331.910301e+1119103.0True13.8833332020-11-03 10:05:302020-11-03 09:54:352020-11-03 09:51:492020-11-03 10:06:032020-11-03 10:05:302020-11-03 10:06:032020-11-03 09:51:492020-11-03 09:54:35correction2021-08-23 13:59:14.704TrueNaNNaN1910301040119103Hills Community Center
16ac91bbf9674101f677eb93eab3d5b8e9zqumhf_generald77a78359a234e91d2167ef7847b6e79HSHS110 Main St, Hills, IA 52235, United States0.00000022.2000006017.3000001.913905e+1119139.0False17.3000002020-11-03 05:54:022020-11-03 05:54:022020-11-03 05:42:342020-11-03 06:04:462020-11-03 05:54:022020-11-03 06:04:462020-11-03 05:42:342020-11-03 05:54:02correction2021-08-23 13:59:14.704TrueNaNNaN1910301040119103Hills Community Center
29d7b72dd88ad3257ef3c5ba364db11ce9zw39fk_generalfce6d89b09db1889067453187930e4eeWATERLOO 1-6CEDAR VALLEY CHURCH3520 Ansborough Av Waterloo 5070118.21666720.1333336019.2000001.901300e+1119013.0False19.2000002020-11-03 00:10:232020-11-02 23:52:102020-11-02 23:51:292020-11-03 00:11:372020-11-03 00:10:232020-11-03 00:11:372020-11-02 23:51:292020-11-02 23:52:10google_api2021-08-23 13:59:14.704TrueNaNNaN1901300150119013CEDAR VALLEY CHURCH
37f574e294815ffc566b8f9b250d727179zw39fk_generalfce6d89b09db1889067453187930e4eeWATERLOO 1-6CEDAR VALLEY CHURCH3520 Ansborough Av Waterloo 507013.65000019.3666676018.6166671.901300e+1119013.0False18.6166672020-11-03 09:53:092020-11-03 09:49:302020-11-03 09:48:502020-11-03 10:08:122020-11-03 09:53:092020-11-03 10:08:122020-11-03 09:48:502020-11-03 09:49:30google_api2021-08-23 13:59:14.704TrueNaNNaN1901300150119013CEDAR VALLEY CHURCH
4dfb9aa0942eefa2b0c57eb6b2af415db9zw39fk_generalfce6d89b09db1889067453187930e4eeWATERLOO 1-6CEDAR VALLEY CHURCH3520 Ansborough Av Waterloo 5070118.93333326.2833336025.1333331.901300e+1119013.0False25.1333332020-11-03 00:01:002020-11-02 23:42:042020-11-02 23:41:452020-11-03 00:08:022020-11-03 00:01:002020-11-03 00:08:022020-11-02 23:41:452020-11-02 23:42:04google_api2021-08-23 13:59:14.704TrueNaNNaN1901300150119013CEDAR VALLEY CHURCH
542c46d63defdcfafc3f075086ad2610b9zw39fk_generalfce6d89b09db1889067453187930e4eeWATERLOO 1-6CEDAR VALLEY CHURCH3520 Ansborough Av Waterloo 507010.00000017.5333336017.3666671.901300e+1119013.0False17.3666672020-11-03 08:17:372020-11-03 08:17:372020-11-03 08:07:482020-11-03 08:25:202020-11-03 08:17:372020-11-03 08:25:202020-11-03 08:07:482020-11-03 08:17:37google_api2021-08-23 13:59:14.704TrueNaNNaN1901300150119013CEDAR VALLEY CHURCH
6f8e098bfd5c3ffd584806274f52fab929zw1zge_general6d9fdc19c4255c51ed316f5c844c9821X403NaN2501 HUDSON RD (SW ENTRANCE), CEDAR FALLS, IA 5061422.88333323.2500006023.1833331.901300e+1119013.0True23.1833332020-11-03 09:37:432020-11-03 09:14:502020-11-03 09:14:282020-11-03 09:37:432020-11-03 09:37:432020-11-03 09:37:432020-11-03 09:14:282020-11-03 09:14:50cpi2021-08-23 13:59:14.704TrueNaNNaN1901300260519013UNI-DOME
7199a2e2964714fbb7c0ba574b12747509zw1zgs_general0ff583215a9f73222b20dbda7d4aa21eCEDAR FALLS 1-2Precinct X1022501 Hudson Rd, Cedar Falls, IA 50614, United States10.40000015.0000006014.7333331.915737e+1119157.0True14.7333332020-11-03 02:25:242020-11-03 02:15:002020-11-03 02:10:242020-11-03 02:25:242020-11-03 02:25:242020-11-03 02:25:242020-11-03 02:10:242020-11-03 02:15:00correction2021-08-23 13:59:14.704TrueNaNNaN1901300260519013UNI-DOME
8ad581f3f7cb82f32cebb5941e812a99e9ze3x7s_general167bae45d46c7ab8531cac047c2b45f32222 Morningside Branch Library4005 Morningside Ave, Sioux City, IA 51106, United States0.0000008.500000603.950000NaNNaNFalse3.9500002020-11-03 11:44:182020-11-03 11:44:182020-11-03 11:44:182020-11-03 11:52:482020-11-03 11:44:182020-11-03 11:52:482020-11-03 11:44:182020-11-03 11:44:18correction2021-08-23 13:59:14.704TrueNaNNaN191930020001919322 MORNINGSIDE BRANCH LIBRARY
90cd90738122e5a55896f1b11238bfb909ze3x7s_general167bae45d46c7ab8531cac047c2b45f32222 Morningside Branch Library4005 Morningside Ave, Sioux City, IA 51106, United States1.0833336.933333606.4000001.919300e+1119193.0False6.4000002020-11-03 10:02:112020-11-03 10:01:062020-11-03 09:56:202020-11-03 10:03:162020-11-03 10:02:112020-11-03 10:03:162020-11-03 09:56:202020-11-03 10:01:06correction2021-08-23 13:59:14.704TrueNaNNaN191930020001919322 MORNINGSIDE BRANCH LIBRARY

Last rows

DEVICE_ID_HASHSPATIALLY_DISTINCT_GEOHASH_KEYCND_POLL_UUIDPRECINCT_IDPRECINCT_NAMEPOLLING_LOCATION_ADDRESSLOWER_MINUTES_WAITINGUPPER_MINUTES_WAITINGRADIUS_FROM_POLL_USED_FOR_CALCULATIONEXPECTED_MINUTES_WAITINGHOME_GEOIDHOME_COUNTY_FIPSHAS_PING_IN_BUILDINGWAIT_TIME_MINUTESLAST_PING_IN_CLUSTER_LOCAL_TIMEFIRST_PING_IN_CLUSTER_LOCAL_TIMELAST_PING_IN_PREVIOUS_CLUSTER_LOCAL_TIMEFIRST_PING_IN_NEXT_CLUSTER_LOCAL_TIMEEARLIEST_TIME_LEFT_POLLS_LOCAL_TIMELATEST_TIME_LEFT_POLLS_LOCAL_TIMEEARLIEST_TIME_ARRIVED_POLLS_LOCAL_TIMELATEST_TIME_ARRIVED_POLLS_LOCAL_TIMEPOLLING_LOCATION_SOURCECALCULATED_ATVISIT_DURING_OPEN_HOURSTIMESTAMP_OPEN_LOCALTIMESTAMP_CLOSE_LOCALPOLLING_LOCATION_CENSUS_TRACTPOLLING_LOCATION_COUNTY_FIPSPOLLING_LOCATION_NAME
15443f821d481e3db00c0d516f0f6a3e9a6149zmmmvt_generala825b622c305e05a81014647c965e526ANK 10NaN400 NW LAKESHORE DR., ANKENY, IA 500230.1333333.716667603.6166671.915301e+1119153.0False3.6166672020-11-03 01:27:082020-11-03 01:27:002020-11-03 01:26:172020-11-03 01:30:002020-11-03 01:27:082020-11-03 01:30:002020-11-03 01:26:172020-11-03 01:27:00cpi2021-08-23 13:59:14.704TrueNaNNaN1915301020519153093 LAKESIDE CENTER
15444be9b5e36c273fc3089048cc454c74f649zmmekr_general0343b4b0c3d29edbf59abbf17b46c276MADISONMadison 1 (133)309 Van Dorn St, Polk City, IA 50226, United States0.0000008.150000605.0166671.904905e+1119049.0False5.0166672020-11-03 05:31:472020-11-03 05:31:472020-11-03 05:27:432020-11-03 05:35:522020-11-03 05:31:472020-11-03 05:35:522020-11-03 05:27:432020-11-03 05:31:47correction2021-08-23 13:59:14.704TrueNaNNaN1915301150019153133 POLK CITY COMMUNITY BUILDING
15445f16e2b719b2383e2304d8dab57b07cc89zrk8uq_general1de15e8d2803191b57533485833eaf11WILTON/MOSCOWWilton Twp/Moscow1215 Cypress St, Wilton, IA 52778, United States12.00000012.0000006012.0000001.913905e+1119139.0True12.0000002020-11-03 01:39:002020-11-03 01:27:002020-11-03 01:27:002020-11-03 01:39:002020-11-03 01:39:002020-11-03 01:39:002020-11-03 01:27:002020-11-03 01:27:00correction2021-08-23 13:59:14.704TrueNaNNaN1913905020019139WILTON COMM CENTER TWP
15446c6ca6b9b2b4d5ae6b9bf404feb563c479z7fezy_generalccaa5283b4557ea3785fad23058407e2CB 10ACouncil Bluffs 10A30 Valley View Dr, Council Bluffs, IA 51503, United States0.78333313.1500006012.9166671.915503e+1119155.0True12.9166672020-11-03 10:02:382020-11-03 10:01:512020-11-03 10:00:312020-11-03 10:13:402020-11-03 10:02:382020-11-03 10:13:402020-11-03 10:00:312020-11-03 10:01:51correction2021-08-23 13:59:14.704TrueNaNNaN191550317001915510A NEW HORIZON PRESBYTERIAN CHURCH
1544718e53ea3be2ae4c8319a663194b0fc159z7fezy_generalccaa5283b4557ea3785fad23058407e2CB 10ACouncil Bluffs 10A30 Valley View Dr, Council Bluffs, IA 51503, United States7.7833338.200000608.0000001.915503e+1119155.0False8.0000002020-11-03 10:09:262020-11-03 10:01:392020-11-03 10:01:222020-11-03 10:09:342020-11-03 10:09:262020-11-03 10:09:342020-11-03 10:01:222020-11-03 10:01:39correction2021-08-23 13:59:14.704TrueNaNNaN191550317001915510A NEW HORIZON PRESBYTERIAN CHURCH
15448ab88e793f82ef482827c8ca2c05588459z7fezy_generalccaa5283b4557ea3785fad23058407e2CB 10ACouncil Bluffs 10A30 Valley View Dr, Council Bluffs, IA 51503, United States9.16666719.7666676017.4333331.915503e+1119155.0False17.4333332020-11-03 09:59:202020-11-03 09:50:102020-11-03 09:45:292020-11-03 10:05:152020-11-03 09:59:202020-11-03 10:05:152020-11-03 09:45:292020-11-03 09:50:10correction2021-08-23 13:59:14.704TrueNaNNaN191550317001915510A NEW HORIZON PRESBYTERIAN CHURCH
154497c302ec472d9daff80ceae00a36190df9zm7q2z_generalb8cd96df4ccac702ca7c404ca9c30d69IN5INDIANOLA 31606 IA-92, Indianola, IA 50125, United States8.01666712.0000006011.4833331.918102e+1119181.0True11.4833332020-11-03 00:36:412020-11-03 00:28:402020-11-03 00:27:432020-11-03 00:39:432020-11-03 00:36:412020-11-03 00:39:432020-11-03 00:27:432020-11-03 00:28:40correction2021-08-23 13:59:14.704TrueNaNNaN1918102090019181MASONIC HALL
1545039fd915012eccc1d23728d3f116fe1b69zmktdk_general0abb3d548a237a9919f032786a4391adDM 055Des Moines 055120 2nd Ave A, Des Moines, IA 50309, United States0.00000018.7166676015.166667NaNNaNFalse15.1666672020-11-03 03:17:472020-11-03 03:17:472020-11-03 03:05:282020-11-03 03:24:112020-11-03 03:17:472020-11-03 03:24:112020-11-03 03:05:282020-11-03 03:17:47correction2021-08-23 13:59:14.704TrueNaNNaN1915300510119153055 POLK COUNTY ELECTION OFFICE
154515aa75d83dca019e6928c29d48813568c9zmktdk_general0abb3d548a237a9919f032786a4391adDM 055Des Moines 055120 2nd Ave A, Des Moines, IA 50309, United States0.0000005.766667605.5666671.915300e+1119153.0False5.5666672020-11-03 11:50:132020-11-03 11:50:132020-11-03 11:45:112020-11-03 11:50:572020-11-03 11:50:132020-11-03 11:50:572020-11-03 11:45:112020-11-03 11:50:13correction2021-08-23 13:59:14.704TrueNaNNaN1915300510119153055 POLK COUNTY ELECTION OFFICE
154522f8426989387b6c86e824a971befdb299zmktdk_general0abb3d548a237a9919f032786a4391adDM 055Des Moines 055120 2nd Ave A, Des Moines, IA 50309, United States0.0000004.150000604.0666671.915301e+1119153.0True4.0666672020-11-03 03:33:202020-11-03 03:33:202020-11-03 03:32:002020-11-03 03:36:092020-11-03 03:33:202020-11-03 03:36:092020-11-03 03:32:002020-11-03 03:33:20correction2021-08-23 13:59:14.704TrueNaNNaN1915300510119153055 POLK COUNTY ELECTION OFFICE